Triple
T5022388
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Temple University station |
E112883
|
entity |
| Predicate | servesLine |
P839
|
FINISHED |
| Object | Media/Wawa Line |
E16839
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Media/Wawa Line | Statement: [Temple University station, servesLine, Media/Wawa Line]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Media/Wawa Line Context triple: [Temple University station, servesLine, Media/Wawa Line]
-
A.
Media/Wawa Line
chosen
The Media/Wawa Line is a commuter rail service in the Philadelphia region that connects Center City with suburban communities in Delaware County, Pennsylvania.
-
B.
WAWA
WAWA is the station code for Wawa railway station, a train stop located in Wawa, New South Wales, Australia.
-
C.
Aqua Line
Aqua Line is one of the main corridors of the Nagpur Metro rapid transit system in Nagpur, India.
-
D.
Kada Line
The Kada Line is a regional railway line in Wakayama Prefecture, Japan, providing local passenger service between Wakayamashi and Kada along the coast.
-
E.
Wenhu line
The Wenhu line is a driverless, medium-capacity rapid transit line in the Taipei Metro system that connects key districts across Taipei and New Taipei City.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69bd4435c2f48190be593158cbfcf8a3 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd73656edc8190b802ad38d9552b58 |
completed | March 20, 2026, 4:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be9282e2b08190abfa2c3e450957d4 |
completed | March 21, 2026, 12:43 p.m. |
Created at: March 20, 2026, 1:36 p.m.